Back

ReMind: A Retrospective Self-Report Paradigm for Studying Mind-Wandering Onset During Reading

Sun, H.; Birney, A.; Singh, N.; Olszko, A.; Chen, P.; Ke, J.; Rosenberg, M. D.; Jangraw, D. C.

2026-05-18 bioengineering
10.64898/2026.05.14.725227 bioRxiv
Show abstract

Mind-wandering (MW) is a frequent and pervasive phenomenon, yet it is commonly assessed using self-reports or probe-based methods that offer limited temporal precision regarding its onset. In this study, we introduce a novel paradigm, ReMind, that estimates the onset and duration of MW episodes during natural reading by combining retrospective self-reports with eye-tracking. Participants indicated the words where they believed their mind started and stopped wandering, and these reports were aligned with gaze timestamps to estimate MW onset. Using data from 44 participants, we examined whether knowledge of MW onset improves the detection of MW from eye-tracking signals. To evaluate relevance for both self-report and thought-probe paradigms, we additionally simulated thought probes by randomly sampling time points during reading. Logistic regression classifiers trained on eye-tracking features extracted from time windows anchored to MW onset achieved AUROC scores of 0.659 and 0.621 under the self-report and simulated thought-probe paradigms, respectively, using leave-one-subject-out cross-validation. In both cases, onset-aligned windows outperformed classifiers trained using arbitrary MW windows. Sliding-window analyses further revealed systematic temporal changes around MW onset, with classification performance peaking at approximately 3 seconds after onset. Feature-level analyses showed reduced fixation rate and fixation dispersion, along with increased pupil size following MW onset. Together, these findings characterize the temporal progression from on-task reading to MW. Overall, ReMind provides a useful framework for studying the temporal dynamics of MW during naturalistic reading.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Scientific Reports
3102 papers in training set
Top 0.4%
22.4%
2
Journal of The Royal Society Interface
189 papers in training set
Top 0.3%
8.4%
3
Journal of Vision
92 papers in training set
Top 0.1%
8.4%
4
PLOS ONE
4510 papers in training set
Top 25%
6.8%
5
Computers in Biology and Medicine
120 papers in training set
Top 0.2%
6.8%
50% of probability mass above
6
Behavior Research Methods
25 papers in training set
Top 0.1%
4.8%
7
Advanced Science
249 papers in training set
Top 5%
3.9%
8
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 18%
3.9%
9
Journal of Neural Engineering
197 papers in training set
Top 0.7%
3.6%
10
European Journal of Neuroscience
168 papers in training set
Top 0.1%
3.1%
11
IEEE Access
31 papers in training set
Top 0.3%
1.8%
12
Nature Communications
4913 papers in training set
Top 52%
1.7%
13
eneuro
389 papers in training set
Top 6%
1.7%
14
Philosophical Transactions of the Royal Society B
51 papers in training set
Top 3%
1.7%
15
Communications Biology
886 papers in training set
Top 13%
1.3%
16
iScience
1063 papers in training set
Top 22%
1.2%
17
Frontiers in Human Neuroscience
67 papers in training set
Top 2%
0.9%
18
Journal of Neuroscience Methods
106 papers in training set
Top 1%
0.9%
19
Journal of NeuroEngineering and Rehabilitation
28 papers in training set
Top 0.8%
0.9%
20
Bioengineering
24 papers in training set
Top 1%
0.9%
21
PLOS Computational Biology
1633 papers in training set
Top 23%
0.8%
22
Journal of Medical Internet Research
85 papers in training set
Top 5%
0.7%
23
Royal Society Open Science
193 papers in training set
Top 5%
0.7%
24
npj Digital Medicine
97 papers in training set
Top 4%
0.6%